A New, Interactive Approach to Learning Data Science

This repository is a related to all about Deep Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)

Nudity/pornography detection using deeplearning. This model is trained using pretrained VGG-16. To know more about this check the readme file below

ML-powered Loan-Marketer Customer Filtering Engine

This nuget package is designed to help you easily identify and detect profanity or bad words within a given sentence or string. It works under a simple binary classifier that has been built and trained using ML.NET for accurate and efficient detection of inappropriate language.

Malaria is a serious global health problem that affects millions of people each year. One of the challenges in diagnosing malaria is identifying infected cells from microscopic images of blood smears. Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that have been used for image classification tasks etc

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

Designing your first machine learning pipeline with few lines of codes using Orchest. You will learn to preprocess the data, train the machine learning model, and evaluate the results.

This project demonstrates the implementation of the Perceptron algorithm for binary classification tasks. It includes various advanced features such as data augmentation, feature engineering, and deep learning techniques to enhance model performance and robustness.

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

A Binary Classification application to predict if a car price is good deal or not based on a set of attributes using FastTree trainer based on the Multiple Additive Regression Trees (MART).

I build the Micrograd autogradient engine, which is a functioning neural network with forward pass, backward propagation, and stochastic gradient descent, all built from scratch. This is derived from the great @karpathy micrograd lecture. Each notebook is complete with Andrei's lecture code and speech, as well as my own code, anecdotes and addition

Classification of two varients of rice - Osmancik, and Cammeo using machine learning

Бинарная классификация пользователей образовательной платформы Stepic на тех, кто скорее всего пройдет курс до конца и тех, кто скорее всего покинет платформу. Модель способна давать предсказания по анализу поведения юзеров на сайте за первые 3 дня

In this we trained a model to detect if there is a tumor in the brain image given to the model. Meaning a model for binary class with an accuracy of above 90 for same and cross validation.

Implementing a model that can verify if two images belongs to same personality or not. Answer the question "Is this the claimed person?" It is a 1:1 matching problem i.e. given a face your task is to compare the candidate face to another and verify whether it is a match or not. My custom CNN model has achieved marvelous performance on the dataset.

Used Machine Learning to create a cryptocurrency classification system for my investment banking client who is interested in offering cryptocurrencies for its customers.